This introduction to geospatial analysis and visualization in R will cover the following topics:
Here’s a great an open-source textbook on the topic: https://geocompr.robinlovelace.net/index.html I derived some of the following excercises from the book.
Please work your way through the tutorial and ask any questions you may have!
P.S. This document was generated using R Markdown. This is a great tool for code transparency and data analysis, because the code blocks, code outputs, and your comments are “knitted” into a single document! Ask us more about this
A GIS or Geographic Information System stores, organizes, manages, creates, processes, analyzes … or does anything to geospatial data! When you create a map, you are most likely using a GIS software to bring in all your data to layer multiple types of information. But before you ever create a map, you often need to process, alter, combine, analyze several data streams to reveal some meaning or answer a question. Sometimes you use a GIS to alter geospatial data so heavily, it ends up being just a number! But it’s always important to understand and visualize where data came from, and especially the characteristics of that data, including resolution, accuracy, age, frequency, and other assumptions.
The best data we
have are still abstractions of the real world. We are limited by the
resolution of our data. The major tenant of GIS is drawing conclusions
about the world based on spatial/topological relationships and
distributions.
Some example questions we might ask using a GIS: What is the shortest route from me to the pizza store? How does rainfall vary with elevation? What land use is most frequented by the Eastern Bluebird? What is the nearest fire hydrant to a house fire?
In your every-day life, you use web platforms like Google Maps or OpenStreetMap to examine data or calculate how you move about the world.
The image above includes the 2 primary types of geospatial data: VECTOR and RASTER.
The following images were made using R! They’re at large geographic scales, but there are so many beautiful opportunities for all scales and data types. The freedom and curse with making maps is unlimited ways to slightly tweak, adjust, emphasize components of the data and labeling. Effective map making is an art form.
Making GIF with R
Climate Data